IRON-MAN: An Approach to Perform Temporal Motionless Analysis of Video Using CNN in MPSoC

This paper proposes a novel human-inspired methodology called IRON-MAN ( I ntegrated R ati ON al prediction and M otionless AN alysis ) for mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video ( TMAV ). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image ( ECTI ) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption and lifespan reliability of the device.